code: def get_score(self, ground_truth, predicted_text):
      all_rouges = []
      for i in range(len(ground_truth)):
          scores = self.rscorers.score(
              predicted_text[i], ground_truth[i]
          )
          all_rouges.append(
              [
                  scores["rouge1"].fmeasure,
                  scores["rouge2"].fmeasure,
                  scores["rouge3"].fmeasure,
                  scores["rougeL"].fmeasure,
              ]
          )
  
      all_rouges = np.mean(np.array(all_rouges), axis=0).tolist()
  
      return {
          "rouge_1_score": torch.tensor(all_rouges[0]),
          "rouge_2_score": torch.tensor(all_rouges[1]),
          "rouge_3_score": torch.tensor(all_rouges[2]),
          "rouge_L_score": torch.tensor(all_rouges[3]),
      }
  
file_path: NeMo/nemo/collections/nlp/metrics/prompt_learning_metrics.py
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from rouge_score import rouge_scorer
  from sacrebleu import BLEU
label_desc: Use image node_id 2 for calling the ROUGEScores obj's underlying member method code with example usage: obj.get_score(ground_truth, predicted_text) and returns: dict